Determining Comprehensiveness of Question Paper Given Syllabus

ABSTRACT

A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms forautomatically determining the comprehensiveness of a question papergiven the syllabus of topics.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems, which may take an input question, analyze it, and returnresults indicative of the most probable answer to the input question. QAsystems provide automated mechanisms for searching through large sets ofsources of content, e.g., electronic documents, and analyze them withregard to an input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypotheses based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypotheses, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States patent application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing system,is provided for determining comprehensiveness of a question paper givena syllabus of topics. The method comprises finding, by an answer andevidence generator of a question answering system executing on the dataprocessing system, one or more answers based on the syllabus of topicsfor each question in the question paper. The method further comprisesidentifying, by the answer and evidence generator, evidence for the oneor more answers in the syllabus for each question in the question paper.The method further comprises identifying, by a concept identifier of thequestion answering system, a set of concepts in the syllabuscorresponding to the evidence for each question in the question paper toform a plurality of sets of concepts. The method further comprisesdetermining a value for a comprehensiveness metric for the questionpaper with respect to the syllabus of topics based on the plurality ofsets of concepts.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIG. 4 is a block diagram illustrating a system for determining thecomprehensiveness of a question paper given the syllabus of topics inaccordance with an illustrative embodiment;

FIG. 5 illustrates a forest of trees of concepts covered by questions ina question paper in accordance with an illustrative embodiment;

FIG. 6 is a flowchart illustrating operation of a mechanism fordetermining coverage of a syllabus by a question paper in accordancewith an illustrative embodiment; and

FIG. 7 is a flowchart illustrating operation of a mechanism fordetermining difficulty of a question paper in accordance with anillustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide a mechanism for determining thecomprehensiveness of a question paper given the syllabus of topics. Asdata processing systems become more fast and powerful and storagedevices increase in capacity, the amount of data available in variousforms and formats is increasing at a tremendous rate. In the educationdomain, individuals not only complete undergraduate or post graduateclasses but also continue learning, which happens in industries or areasof work of the individual. For assessing knowledge, various forms oftests are conducted by presenting question papers to the individuals andthen evaluating their performance based on answers provided.

For effective evaluation, the quality and comprehensiveness or coverageof various topics/aspects of the syllabus is an important factor. Today,there are ways to manually or automatically generate question papers.For example, automated tools may be provided to generate question papersgiven a syllabus of topics. However, a good question paper is one thatcomprehensively covers all of the topics of a given syllabus. Due to alack of tools to evaluate the comprehensiveness of a question paper,people do not consider measuring the comprehensiveness of a test.Rather, the person who designs the test likely follows certainguidelines with the assumption the question paper will cover all thenecessary topics.

The illustrative embodiments provide a tool that automaticallydetermines the comprehensiveness of a question paper or test. Theillustrative embodiments provide a mechanism to evaluate an inputquestion paper against a given syllabus and output a set of measuresindicating various aspects of the paper, such as comprehensiveness,topic coverage, and difficulty. Given the questions, the mechanismattempts to find the most accurate answer and corresponding evidencesfrom the syllabus. Every item of evidence is analyzed to determine theconcept it covers. A concept may be associated with other concepts thatsupport the main concept of the evidence. They syllabus itself isassociated with many topics. The mechanism analyzes the topics andconcepts of a particular question to determine the difficulty level ofthe question in particular and also the complete question set in thepaper.

A “mechanism,” as used herein, may be an implementation of the functionsor aspects of the illustrative embodiments in the form of an apparatus,a procedure, or a computer program product. The mechanisms describedherein may be implemented as specialized hardware, software executing ongeneral purpose hardware, software instructions stored on a medium suchthat the instructions are readily executable by specialized or generalpurpose hardware, a procedure or method for executing the functions, ora combination of the above.

FIGS. 1-3 are directed to describing an example Question/Answer,Question and Answer, or Question Answering (QA) system, methodology, andcomputer program product with which the mechanisms of the illustrativeembodiments may be implemented. As will be discussed in greater detailhereafter, the illustrative embodiments may be integrated in, and mayaugment and extend the functionality of, these QA mechanisms with regardto automatically generating testing/training questions and answers byperforming pattern based analysis and natural language processingtechniques on the given corpus for quick domain adaptation.

Thus, it is important to first have an understanding of how question andanswer creation in a QA system may be implemented before describing howthe mechanisms of the illustrative embodiments are integrated in andaugment such QA systems. It should be appreciated that the QA mechanismsdescribed in FIGS. 1-3 are only examples and are not intended to stateor imply any limitation with regard to the type of QA mechanisms withwhich the illustrative embodiments may be implemented. Manymodifications to the example QA system shown in FIGS. 1-3 may beimplemented in various embodiments of the present invention withoutdeparting from the spirit and scope of the present invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms, whichevaluate the content to identify the most probable answers, i.e.,candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to automaticallygenerate testing/training questions and answers by performing patternbased analysis and natural language processing techniques on the givencorpus for quick domain adaptation.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or more computing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 may include multiple computing devices 104in communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. The QA system 100 andnetwork 102 may enable question/answer (QA) generation functionality forone or more QA system users via their respective computing devices 110,112. Other embodiments of the QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 may be routed through the network 102. The various computing devices104 on the network 102 may include access points for content creatorsand QA system users. Some of the computing devices 104 may includedevices for a database storing the corpus of data 106 (which is shown asa separate entity in FIG. 1 for illustrative purposes only). Portions ofthe corpus of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 may includelocal network connections and remote connections in various embodiments,such that the QA system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108, which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question, which it then parses to extract the major features ofthe question, which in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e., candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline500, i.e., the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpora of data/information 345 inorder to generate one or more hypotheses. The queries may be generatedin any known or later developed query language, such as the StructureQuery Language (SQL), or the like. The queries may be applied to one ormore databases storing information about the electronic texts,documents, articles, websites, and the like, that make up the corpora ofdata/information 345. That is, these various sources themselves,different collections of sources, and the like, may represent adifferent corpus 347 within the corpora 345. There may be differentcorpora 347 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus may then be analyzed and used, during the hypothesis generationstage 340, to generate hypotheses for answering the input question.These hypotheses are also referred to herein as “candidate answers” forthe input question. For any input question, at this stage 340, there maybe hundreds of hypotheses or candidate answers generated that may needto be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs, which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e., a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e., that the candidate answer is the correct answerfor the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question.

After stage 380, or as part of stage 380, the set of candidate answersis output via a graphical user interface, which provides the user withtools for collaborating with the QA system to review, evaluate, andmodify the listing of candidate answers and the evidence associated withthese candidate answers that is evaluated by the QA system. That is, atstage 390, the graphical user interface engine not only receives thefinal ranked listing of candidate answers generated by the QA systempipeline 300, but also receives the underlying evidence information foreach of the candidate answers from the hypothesis and evidence scoringstage 350, and uses this information to generate a graphical userinterface outputting the ranked listing of candidate answers and anoutput of the selected portions of the corpus of data/information thatsupports, and/or detracts, from the candidate answers being the correctanswer for the input question, referred to hereafter as the “evidencepassages.” Stage 390 may also cache candidate answers and evidence in QAcache 395 to more quickly provide answers and supporting evidence forrecently or frequently asked questions.

FIG. 4 is a block diagram illustrating a system for determining thecomprehensiveness of a question paper given the syllabus of topics inaccordance with an illustrative embodiment. Question answering (QA)system 400 receives question paper 401 and syllabus of topics 402.Question paper 401 consists of a plurality of questions (q₁, . . . ,q_(m)) for testing an individual's understanding or comprehension of thetopics of syllabus 402, which comprises all the relevant documents andtopics of a domain. Syllabus 402 may also be referred to as a corpus.Syllabus 402 is in the form of text consisting of topics (t₁, . . . ,t_(n)).

QA system 400 determines how comprehensive the question paper 401 iswith respect to syllabus 402 and the difficulty level and amount ofsyllabus 402 covered by question paper 401. Answer and evidence (AE)generator 403 receives question paper 401 and syllabus 402 and for eachquestion in question paper 401, attempts to find the most accurateanswer and corresponding evidences from syllabus 402. Concept identifier404 analyzes the evidence in syllabus 402 to determine the conceptscovered by the answers and evidences.

Concept-to-topic mapper 405 associates each concept with other conceptsthat support the main concept of the evidence. For the identifiedconcepts, concept-to-topic mapper 405 maps the concepts back to thetopic to which they belong. For example, if the topic span from textlocation (1, 2, . . . , i, i+1, . . . , n) and concept spans from (i, .. . , i+k), which is a subset of the previous set, then concept-to-topicmapper 405 would derive that concept belongs to that topic.

Difficulty and coverage calculator 406 determines the coverage ofsyllabus 402 by question paper 401 and the difficulty of the questionpaper 401 and outputs the coverage/difficulty 407. Thecoverage/difficulty output 407 may indicate, for example, how difficultthe question paper is (e.g., a score between 1 and 10), how much of thesyllabus is covered (e.g., a score between 1 and 10 or a percentage),whether each individual topic is covered by the question paper, etc.

More specifically, to compute the topic coverage of a question, q_(i),the mechanism of the illustrative embodiment finds the answer, A(q_(i)),(or set of answers) from the syllabus with the highest confidence usingQA system 400. The mechanism then identifies the evidence of the answer.Then, the mechanism identifies all concepts in the evidence of answersand all concepts supporting the concepts, C(A(qi)), which are consideredto be covered by question q_(i). The mechanism then determines thecoverage of the syllabus, S, by a question paper, QP, as follows:

${{{Coverage}\left( {{QP},S} \right)} = \frac{{Length}\left( {{Union}_{i}{C\left( {A\left( q_{i} \right)} \right)}} \right)}{{Length}(S)}},$

where Length is defined in terms of characters, words, sentences ortuples in the syllabus, S.

The mechanism determines the difficulty of the question paper by firstdetermining that the depth of a covered topic is a function of thecomplexity of the concepts related to that topic. Given a question qi,the mechanism first builds a tree with the central topic t_(i) as a rootnode and tj as a child node if concepts of t_(j) help in understandingthe concept of t_(i). The mechanism then builds this tree until themechanism reaches the fundamental concepts covered by the topic t_(k),which is a leaf node. The mechanism determines the difficulty of aquestion as follows:

Difficulty(q _(i) ,t _(i))=Depth(Tr(t _(i) ,t _(k)),

where Tr is the tree as described above. The mechanism then determinesthe difficulty of the question paper as follows:

Difficulty(QP,S)=Depth(forest:Tr _(i . . . n))

where the forest:Tr_(i . . . n) is the combination of all trees builtfrom the questions in the question paper, perhaps pruned, and the depthis the depth of deepest tree.

In alternative embodiments, the mechanism may determine the difficultyto be a function of the depths of the trees corresponding to thequestions of the question paper. For example, the mechanism maydetermine the difficulty of the question paper to be an average of thetree depths, a median of the tree depths, or some other functionrepresenting an overall assessment of the depth of the concepts coveredby the questions in the question paper.

FIG. 5 illustrates a forest of trees of concepts covered by questions ina question paper in accordance with an illustrative embodiment.Complexity of a concept depends on where it is first defined, how manyother concepts are used to define this concept, and their complexity(depth of the topic in the scope of the text). The complexity of aquestion, q_(i), depends on the complexity of the topics in C(A(q_(i))).

Thus, in the example shown in FIG. 5, if the answer to a question isfound in topic 1, and evidence and support for the answer exists intopic 3 and topic 6, then depth of this topic would be 3 and thedifficulty of the question would be 3. If evidence and support for theanswer exists in topic 3, topic 5, and topic 9, then the depth of thistopic would be 4 and the difficulty of the question would be 4.

In the depicted example, the topics 1-16 may make up a forest for thetrees built based on questions of a question paper. In this example, themaximum depth is 4; therefore, the difficulty of the question paperwould be 4.

As an example, consider a “Virtual Memory” topic in a syllabus entitled,“Operating System Concepts.” The mechanism of the illustrativeembodiments may be given this syllabus and a question paper. The topic,“Virtual Memory,” may cover the following concepts: Multiprogramming,Paging, Page Replacement Algorithms (LRU, FIFO, etc.), Thrashing, PageFault, Belady's Anomaly, Locality of Reference, Memory Mapped IO, and soon. Now, assume the question paper has many questions, one of which isrelated to Belady's Anomaly. For answering this question, an individualmust have knowledge of concepts like Paging, Page Fault, and Belady'sAnomaly. The Breadth/Coverage of the “Virtual Memory” topic covered bythis question would be a function of the coverage of the conceptsPaging, Page Fault, and Belady's Anomaly in the overall syllabus. Thedepth/complexity of the “Virtual Memory” topic covered by this questionwould be a function of the complexity of the concepts Paging, PageFault, and Belady's Anomaly in the overall syllabus.

An output for this question for the “Virtual Memory” topic would be Xbreadth/coverage and Y depth/complexity for all the measures. As a finaloutput, the comprehensiveness for the question paper would be a functionof the output of each measure for all the questions of the questionpaper.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in any one or more computer readablemedium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium is a system, apparatus, or device of an electronic,magnetic, optical, electromagnetic, or semiconductor nature, anysuitable combination of the foregoing, or equivalents thereof. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical device havinga storage capability, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiberbased device, a portable compact disc read-only memory (CDROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium is any tangible medium that can containor store a program for use by, or in connection with, an instructionexecution system, apparatus, or device.

In some illustrative embodiments, the computer readable medium is anon-transitory computer readable medium. A non-transitory computerreadable medium is any medium that is not a disembodied signal orpropagation wave, i.e. pure signal or propagation wave per se. Anon-transitory computer readable medium may utilize signals andpropagation waves, but is not the signal or propagation wave itself.Thus, for example, various forms of memory devices, and other types ofsystems, devices, or apparatus, that utilize signals in any way, suchas, for example, to maintain their state, may be considered to benon-transitory computer readable media within the scope of the presentdescription.

A computer readable signal medium, on the other hand, may include apropagated data signal with computer readable program code embodiedtherein, for example, in a baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, including, but notlimited to, electro-magnetic, optical, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Similarly, a computer readable storage medium is any computer readablemedium that is not a computer readable signal medium.

Computer code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination thereof

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to the illustrativeembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 6 is a flowchart illustrating operation of a mechanism fordetermining coverage of a syllabus by a question paper in accordancewith an illustrative embodiment. Operation begins (block 600), and foreach question (block 601), the mechanism finds an answer from thesyllabus with a highest confidence (block 602). The mechanism determinesevidence of the answer in topics and concepts of the syllabus (block603). The mechanism then determines whether the question is the lastquestion in the question paper (block 604). If the question is not thelast question, operation returns to block 601 to consider the nextquestion.

If the question is the last question in the question paper in block 604,the mechanism determines coverage of the syllabus by the question paper(block 605). The mechanism determines the coverage of the syllabus bydetermining a number of concepts providing evidence for or supportingthe answers of the questions relative to a number of concepts in thesyllabus. Thereafter, operation ends (block 606).

In one embodiment, the mechanism determines for each question theconcepts in the evidence of the answer to the question and all theconcepts that support those concepts. The mechanism sets those conceptsas the concepts covered by the question. The mechanism then determines aunion of all of concepts covered by the questions in the question paper.The mechanism determines a length of union of the concepts covered bythe question paper and divides that by the length of the syllabus. Themechanism may determine length in terms of characters, words, sentences,or tuples.

FIG. 7 is a flowchart illustrating operation of a mechanism fordetermining difficulty of a question paper in accordance with anillustrative embodiment. Operation begins (block 700), and for eachquestion (block 701), the mechanism finds an answer from the syllabuswith a highest confidence (block 702). The mechanism builds a tree oftopics contributing to the answer (block 703) and determines a depth ofthe tree (block 704). Then, the mechanism determines the difficulty ofthe question to be the depth of the tree (block 705). The mechanism thendetermines whether the question is the last question in the questionpaper (block 706). If the question is not the last question, operationreturns to block 701 to consider the next question.

If the question is the last question in the question paper in block 706,the mechanism builds a forest of the trees (block 707). The mechanismperforms tree pruning (block 708). The mechanism then determines a depthof the forest (block 709). The mechanism determines the difficulty ofthe question paper to be the depth of the forest (block 710).Thereafter, operation ends (block 711).

In alternative embodiments, the mechanism may determine the difficultyto be a function of the depths of the trees corresponding to thequestions of the question paper. For example, the mechanism maydetermine the difficulty of the question paper to be an average of thetree depths, a median of the tree depths, or some other functionrepresenting an overall assessment of the depth of the concepts coveredby the questions in the question paper.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, in a data processing system, fordetermining comprehensiveness of a question paper given a syllabus oftopics, the method comprising: finding, by an answer and evidencegenerator of a question answering system executing on the dataprocessing system, one or more answers based on the syllabus of topicsfor each question in the question paper; identifying, by the answer andevidence generator, evidence for the one or more answers in the syllabusfor each question in the question paper; identifying, by a conceptidentifier of the question answering system, a set of concepts in thesyllabus corresponding to the evidence for each question in the questionpaper to form a plurality of sets of concepts; and determining a valuefor a comprehensiveness metric for the question paper with respect tothe syllabus of topics based on the plurality of sets of concepts. 2.The method of claim 1, wherein finding the one or more answers in thesyllabus of topics for each question in the question paper comprisesusing the question answering system to find one or more answers having ahighest confidence score for each question in the question paper.
 3. Themethod of claim 1, wherein determining the set of concepts for a givenquestion comprises determining all evidence concepts in the evidence ofthe one or more answers and all support concepts that support theevidence concepts, wherein the set of concepts comprises the evidenceconcepts and the support concepts.
 4. The method of claim 1, wherein thecomprehensiveness metric comprises a coverage metric, whereindetermining the value for the comprehensiveness metric comprises:determining a union of the plurality of sets of concepts; determining avalue of the coverage metric for the question paper to be equal to alength of the union of the plurality of sets of concepts divided by alength of the syllabus to form a result.
 5. The method of claim 1,wherein the comprehensiveness metric comprises a difficulty metric,wherein determining the value for the comprehensiveness metriccomprises: mapping the sets of concepts to topics in the syllabus; foreach given question in the question paper, building a tree of topicscomprising a root node representing a central topic, at least one childnode representing a topic having concepts that help in understandingconcepts of the central topic, and at least one leaf node representing atopic having fundamental concepts; and determining a value of adifficulty metric for the given question to be equal to a depth of thetree.
 6. The method of claim 5, wherein determining the value for thecomprehensiveness metric further comprises: building a forest of thetrees corresponding to the questions of the question paper; anddetermining value of the difficulty metric for the question paper to beequal to a depth of the forest.
 7. A computer program product comprisinga computer readable storage medium having a computer readable programstored therein, wherein the computer readable program, when executed ona computing device, causes the computing device to: find, by an answerand evidence generator of a question answering system executing on thedata processing system, one or more answers based on the syllabus oftopics for each question in the question paper; identify, by the answerand evidence generator, evidence for the one or more answers in thesyllabus for each question in the question paper; identify, by a conceptidentifier of the question answering system, a set of concepts in thesyllabus corresponding to the evidence for each question in the questionpaper to form a plurality of sets of concepts; and determine a value fora comprehensiveness metric for the question paper with respect to thesyllabus of topics based on the plurality of sets of concepts.
 8. Thecomputer program product of claim 7, wherein finding the one or moreanswers in the syllabus of topics for each question in the questionpaper comprises using the question answering system to find one or moreanswers having a highest confidence score for each question in thequestion paper.
 9. The computer program product of claim 7, whereindetermining the set of concepts for a given question comprisesdetermining all evidence concepts in the evidence of the one or moreanswers and all support concepts that support the evidence concepts,wherein the set of concepts comprises the evidence concepts and thesupport concepts.
 10. The computer program product of claim 7, whereinthe comprehensiveness metric comprises a coverage metric, whereindetermining the value for the comprehensiveness metric comprises:determining a union of the plurality of sets of concepts; determining avalue of the coverage metric for the question paper to be equal to alength of the union of the plurality of sets of concepts divided by alength of the syllabus to form a result.
 11. The computer programproduct of claim 7, wherein the comprehensiveness metric comprises adifficulty metric, wherein determining the value for thecomprehensiveness metric comprises: mapping the sets of concepts totopics in the syllabus; for each given question in the question paper,building a tree of topics comprising a root node representing a centraltopic, at least one child node representing a topic having concepts thathelp in understanding concepts of the central topic, and at least oneleaf node representing a topic having fundamental concepts; anddetermining a value of a difficulty metric for the given question to beequal to a depth of the tree.
 12. The computer program product of claim11, wherein determining the value for the comprehensiveness metricfurther comprises: building a forest of the trees corresponding to thequestions of the question paper; and determining value of the difficultymetric for the question paper to be equal to a depth of the forest. 13.The computer program product of claim 7, wherein the computer readableprogram is stored in a computer readable storage medium in a dataprocessing system and wherein the computer readable program wasdownloaded over a network from a remote data processing system.
 14. Thecomputer program product of claim 7, wherein the computer readableprogram is stored in a computer readable storage medium in a server dataprocessing system and wherein the computer readable program isdownloaded over a network to a remote data processing system for use ina computer readable storage medium with the remote system.
 15. Anapparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory comprises instructions which, whenexecuted by the processor, cause the processor to: find, by an answerand evidence generator of a question answering system executing on thedata processing system, one or more answers based on the syllabus oftopics for each question in the question paper; identify, by the answerand evidence generator, evidence for the one or more answers in thesyllabus for each question in the question paper; identify, by a conceptidentifier of the question answering system, a set of concepts in thesyllabus corresponding to the evidence for each question in the questionpaper to form a plurality of sets of concepts; and determine a value fora comprehensiveness metric for the question paper with respect to thesyllabus of topics based on the plurality of sets of concepts.
 16. Theapparatus of claim 15, wherein finding the one or more answers in thesyllabus of topics for each question in the question paper comprisesusing the question answering system to find one or more answers having ahighest confidence score for each question in the question paper. 17.The apparatus of claim 15, wherein determining the set of concepts for agiven question comprises determining all evidence concepts in theevidence of the one or more answers and all support concepts thatsupport the evidence concepts, wherein the set of concepts comprises theevidence concepts and the support concepts.
 18. The apparatus of claim15, wherein the comprehensiveness metric comprises a coverage metric,wherein determining the value for the comprehensiveness metriccomprises: determining a union of the plurality of sets of concepts;determining a value of the coverage metric for the question paper to beequal to a length of the union of the plurality of sets of conceptsdivided by a length of the syllabus to form a result.
 19. The apparatusof claim 15, wherein the comprehensiveness metric comprises a difficultymetric, wherein determining the value for the comprehensiveness metriccomprises: mapping the sets of concepts to topics in the syllabus; foreach given question in the question paper, building a tree of topicscomprising a root node representing a central topic, at least one childnode representing a topic having concepts that help in understandingconcepts of the central topic, and at least one leaf node representing atopic having fundamental concepts; and determining a value of adifficulty metric for the given question to be equal to a depth of thetree.
 20. The apparatus of claim 19, wherein determining the value forthe comprehensiveness metric further comprises: building a forest of thetrees corresponding to the questions of the question paper; anddetermining value of the difficulty metric for the question paper to beequal to a depth of the forest.